
// g++ -DNDEBUG -O3 -I.. benchEigenSolver.cpp  -o benchEigenSolver && ./benchEigenSolver
// options:
//  -DBENCH_GMM
//  -DBENCH_GSL -lgsl /usr/lib/libcblas.so.3
//  -DEIGEN_DONT_VECTORIZE
//  -msse2
//  -DREPEAT=100
//  -DTRIES=10
//  -DSCALAR=double

#include <iostream>

#include <Eigen/Core>
#include <Eigen/QR>
#include <bench/BenchUtil.h>
using namespace Eigen;

#ifndef REPEAT
#define REPEAT 1000
#endif

#ifndef TRIES
#define TRIES 4
#endif

#ifndef SCALAR
#define SCALAR float
#endif

typedef SCALAR Scalar;

template<typename MatrixType>
__attribute__((noinline)) void
benchEigenSolver(const MatrixType& m)
{
	int rows = m.rows();
	int cols = m.cols();

	int stdRepeats = std::max(1, int((REPEAT * 1000) / (rows * rows * sqrt(rows))));
	int saRepeats = stdRepeats * 4;

	typedef typename MatrixType::Scalar Scalar;
	typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType;

	MatrixType a = MatrixType::Random(rows, cols);
	SquareMatrixType covMat = a * a.adjoint();

	BenchTimer timerSa, timerStd;

	Scalar acc = 0;
	int r = internal::random<int>(0, covMat.rows() - 1);
	int c = internal::random<int>(0, covMat.cols() - 1);
	{
		SelfAdjointEigenSolver<SquareMatrixType> ei(covMat);
		for (int t = 0; t < TRIES; ++t) {
			timerSa.start();
			for (int k = 0; k < saRepeats; ++k) {
				ei.compute(covMat);
				acc += ei.eigenvectors().coeff(r, c);
			}
			timerSa.stop();
		}
	}

	{
		EigenSolver<SquareMatrixType> ei(covMat);
		for (int t = 0; t < TRIES; ++t) {
			timerStd.start();
			for (int k = 0; k < stdRepeats; ++k) {
				ei.compute(covMat);
				acc += ei.eigenvectors().coeff(r, c);
			}
			timerStd.stop();
		}
	}

	if (MatrixType::RowsAtCompileTime == Dynamic)
		std::cout << "dyn   ";
	else
		std::cout << "fixed ";
	std::cout << covMat.rows() << " \t" << timerSa.value() * REPEAT / saRepeats << "s \t"
			  << timerStd.value() * REPEAT / stdRepeats << "s";

#ifdef BENCH_GMM
	if (MatrixType::RowsAtCompileTime == Dynamic) {
		timerSa.reset();
		timerStd.reset();

		gmm::dense_matrix<Scalar> gmmCovMat(covMat.rows(), covMat.cols());
		gmm::dense_matrix<Scalar> eigvect(covMat.rows(), covMat.cols());
		std::vector<Scalar> eigval(covMat.rows());
		eiToGmm(covMat, gmmCovMat);
		for (int t = 0; t < TRIES; ++t) {
			timerSa.start();
			for (int k = 0; k < saRepeats; ++k) {
				gmm::symmetric_qr_algorithm(gmmCovMat, eigval, eigvect);
				acc += eigvect(r, c);
			}
			timerSa.stop();
		}
		// the non-selfadjoint solver does not compute the eigen vectors
		//     for (int t=0; t<TRIES; ++t)
		//     {
		//       timerStd.start();
		//       for (int k=0; k<stdRepeats; ++k)
		//       {
		//         gmm::implicit_qr_algorithm(gmmCovMat, eigval, eigvect);
		//         acc += eigvect(r,c);
		//       }
		//       timerStd.stop();
		//     }

		std::cout << " | \t" << timerSa.value() * REPEAT / saRepeats << "s"
				  << /*timerStd.value() * REPEAT / stdRepeats << "s"*/ "   na   ";
	}
#endif

#ifdef BENCH_GSL
	if (MatrixType::RowsAtCompileTime == Dynamic) {
		timerSa.reset();
		timerStd.reset();

		gsl_matrix* gslCovMat = gsl_matrix_alloc(covMat.rows(), covMat.cols());
		gsl_matrix* gslCopy = gsl_matrix_alloc(covMat.rows(), covMat.cols());
		gsl_matrix* eigvect = gsl_matrix_alloc(covMat.rows(), covMat.cols());
		gsl_vector* eigval = gsl_vector_alloc(covMat.rows());
		gsl_eigen_symmv_workspace* eisymm = gsl_eigen_symmv_alloc(covMat.rows());

		gsl_matrix_complex* eigvectz = gsl_matrix_complex_alloc(covMat.rows(), covMat.cols());
		gsl_vector_complex* eigvalz = gsl_vector_complex_alloc(covMat.rows());
		gsl_eigen_nonsymmv_workspace* einonsymm = gsl_eigen_nonsymmv_alloc(covMat.rows());

		eiToGsl(covMat, &gslCovMat);
		for (int t = 0; t < TRIES; ++t) {
			timerSa.start();
			for (int k = 0; k < saRepeats; ++k) {
				gsl_matrix_memcpy(gslCopy, gslCovMat);
				gsl_eigen_symmv(gslCopy, eigval, eigvect, eisymm);
				acc += gsl_matrix_get(eigvect, r, c);
			}
			timerSa.stop();
		}
		for (int t = 0; t < TRIES; ++t) {
			timerStd.start();
			for (int k = 0; k < stdRepeats; ++k) {
				gsl_matrix_memcpy(gslCopy, gslCovMat);
				gsl_eigen_nonsymmv(gslCopy, eigvalz, eigvectz, einonsymm);
				acc += GSL_REAL(gsl_matrix_complex_get(eigvectz, r, c));
			}
			timerStd.stop();
		}

		std::cout << " | \t" << timerSa.value() * REPEAT / saRepeats << "s \t" << timerStd.value() * REPEAT / stdRepeats
				  << "s";

		gsl_matrix_free(gslCovMat);
		gsl_vector_free(gslCopy);
		gsl_matrix_free(eigvect);
		gsl_vector_free(eigval);
		gsl_matrix_complex_free(eigvectz);
		gsl_vector_complex_free(eigvalz);
		gsl_eigen_symmv_free(eisymm);
		gsl_eigen_nonsymmv_free(einonsymm);
	}
#endif

	std::cout << "\n";

	// make sure the compiler does not optimize too much
	if (acc == 123)
		std::cout << acc;
}

int
main(int argc, char* argv[])
{
	const int dynsizes[] = { 4, 6, 8, 12, 16, 24, 32, 64, 128, 256, 512, 0 };
	std::cout << "size            selfadjoint       generic";
#ifdef BENCH_GMM
	std::cout << "        GMM++          ";
#endif
#ifdef BENCH_GSL
	std::cout << "       GSL (double + ATLAS)  ";
#endif
	std::cout << "\n";
	for (uint i = 0; dynsizes[i] > 0; ++i)
		benchEigenSolver(Matrix<Scalar, Dynamic, Dynamic>(dynsizes[i], dynsizes[i]));

	benchEigenSolver(Matrix<Scalar, 2, 2>());
	benchEigenSolver(Matrix<Scalar, 3, 3>());
	benchEigenSolver(Matrix<Scalar, 4, 4>());
	benchEigenSolver(Matrix<Scalar, 6, 6>());
	benchEigenSolver(Matrix<Scalar, 8, 8>());
	benchEigenSolver(Matrix<Scalar, 12, 12>());
	benchEigenSolver(Matrix<Scalar, 16, 16>());
	return 0;
}
